Machine learning and materials simulation are aiding in designing more reliable materials, especially in applications where high temperatures and mechanical stress are the norms. A research team led by Lehigh University has developed an innovative approach for predicting abnormal grain growth in polycrystalline materials well before it becomes apparent. The research, published in Nature Computational